Contents

Executive Summary

In autonomous navigation understanding the robot's surrounding environment, as well as its position in this environment, is of paramount importance. This project attempts to leverage the open-source efforts resulting in simultaneous localization and mapping (SLAM) algorithms and use them, in collaboration with the Beagleboard -xm, to develop a 3-D model of the world surrounding the board as it moves through space. Obviously the more (quality) sensory data used in a SLAM algorithm the better the results, but at this time a camera will be the only sensor device, although there is the possibility of incorporating a gyroscope. A primary objective of this project is to test the feasibility of using the Beagleboard -xm as the "brain" for an autonomous quad-copter.

Installation Instructions

Give step by step instructions on how to install your project on the SPEd2 image.